This study evaluates 26 methods across nine models and three datasets, analyzing factors such as IoU thresholds, embedding models, feature fusion techniques, and key parameters, and finds that complexity-based and learning-based methods consistently perform best across all settings.
Yinghua Li, Xueqi Dang, Wendkûuni C. Ouédraogo et al.· Empirical Software Engineeri...· 0 citations
A comprehensive empirical study is conducted that confirms the limitations of MLPrior in trajectory prediction scenarios and demonstrates that TrajectoryTest outperforms all existing test input prioritization techniques, including the state-of-the-art approach MLPrior, multiple confidence-based methods, and the baselin...
Xueqi Dang, Yinghua Li, Wendkûuni C. Ouédraogo et al.· ACM Transactions on Software...· 0 citations
This analysis covers 2,857 report-test-patch triplets from Defects4J and SWT-Bench using two widely adopted instruction-tuned LLMs from distinct model families and finds that both models exhibit systematic optimism relative to humans and only modest rank agreement, motivating bias-aware evaluation.
Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang et al.· arXiv.org· 0 citations
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